Team:ETH Zurich/Model



We developed a detailed mechanistic model of our system that describes the behavior of our sensor, switch and reporter components. We present a novel stochastic model for integrase switches, which is crucial to capture the kinetics and cell-to-cell variability of our system’s active learning. The model is structured in modules to enable simple integration and assessment of alternative components. Each module corresponds to an engineered circuit that can be separately validated by experiments.

The model was critical in choosing between alternative designs, designing experiments and providing a proof of concept. We were able to tune our system thanks to a close interaction between experimentalist and modelers. We identified tunable key parameters using sensitivity analysis and determined optimal parameter ranges: simulations guided us to adjust the sensitivity of our sensors to the physiological concentration ranges (see tuning of the nitric oxide sensor, tuning of the AHL sensor, tuning of the lactate sensor) and to optimize the switch for the expected timescale (see tuning of the switch).

Check our module pages to learn about our results in detail!

Figure 1: Schematic view of the model structure. Each module represents a circuit element.


Due to the complexity of our system, an intuitive design would not be sufficient. Several components must be tuned specifically for our application, and the correct functioning of the candidate designs is not obvious. We developed a hybrid deterministic-stochastic model of our system covering the mechanics of the components we use. The main goals include a model-aided design of the system, tuning of the system components for our application and prediction of the system behavior.

The model matches the modular sructure of the system, making easier to exchange components and test their behavior in the full system. With this feature we were able to quickly test alternative parts like the lactate sensor, that was introduced later during development.

The parameters of the model are strictly based on literature or experimental characterization of the single components. The estiamtion of the parameters uses INSIGHT, a recent method based on Approximate Bayesian Computation (ABC) and flow cytomerty data.1



  • Identify at least one parameter set for which the system shows the desired behavior.
  • Predict qualitatively the response of the system to different inputs.


  • Assist design of the system by comparing the qualitative behavior of different design alternatives.
  • Tune the system for the desired time scale and concentration ranges.


  • Develop a detailed model capturing the kinetics of our integrase-based switch.



We started modeling the system already during the design phase, allowing us to provide a quick proof of concept of the proposed curcuits. Figure 2 shows the functioning of the full system: Pavlov's Coli is encpsulated in a pill and is ingested by the patient. During transit in the gut the system is exposed to the inflammation marker (NO) and the candidate marker (species-specific AHL). In presence of both markers the promoters of the switch start flipping, storing the association event on the DNA. After recovery of the pill, the system is exposed to the candidate marker (AHL) again and since AHL was associated with an inflammation marker, GFP is expressed again.

Figure 2: Simulation of the full system. Once NO and AHL are detected at the same time, our memory element start switching. After recovery of the pill, the system is exposed to AHL again and expresses GFP if NO and AHL were detected.


Thanks to qualitative simulations of the system during the design phase, we managed to get early information about the behavior of the the system. In particular the model advised us on the design of the the position of the integrase gene. Moreover, we were able to identify potential problems (like leakiness and sensitivity) and predict their influence on the final system. We designed and constructed the system under awareness of those critical points.

Since our application targets specific time and concentration ranges we used the model to tune the components of the system. The sensor module was tuned for the relevant concentrations of quorum sensing molecule and the switch kinetics were adapted for the expected time scale of the measurements.

Figure 3: Left: Functional regions of the parameter space satisfying the requirements of our application. Right: The range of parameters for the switch that allows the system to memorize events correctly.


Based on the known mechanics of integrases we developed a novel stochastic model for integrases capturing the mechanics of the switching process. We planned to use the stochastic model for estimating the parameters of the model using the INSIGHT tool. Unfortunately the flow cytometry data of the recombinase couldn't be collected in time for performing the estimation before the wiki freeze.

Figure 4: Stochastic simulation of the switch. The plots show the evolution of the four possible states of a slipping cassette. In this case 1000 trajectories (colored) were simulated. The mean behavior of the population is shown in black.


  • [1] Lillacci, Gabriele, and Mustafa Khammash. "The signal within the noise: efficient inference of stochastic gene regulation models using fluorescence histograms and stochastic simulations." Bioinformatics 29.18 (2013): 2311-2319.

Thanks to the sponsors that supported our project: